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NeuroCOLT
Technical Reports 1994
NC-TR-94-001
Computing over the Reals with Addition and
Order: Higher Complexity Classes
Felipe Cucker and Pascal Koiran
NC-TR-94-002
The Complexity of Learning with Queries
Ricard Gavalda
NC-TR-94-003
Probabilistic Analysis of Learning in Artificial
Neural Networks: The
PAC Model and its Variants
Martin Anthony
NC-TR-94-005
A Weak Version of the Blum, Shub & Smale
Model
Pascal Koiran
NC-TR-94-006
On the complexity of Quadratic Programming
in real number models of computation
K. Meer
NC-TR-94-007
On the power of real Turing machines over
binary inputs
Felipe Cucker and Dima Grigoriev
NC-TR-94-008
Generalized Knapsack Problems
Felipe Cucker
NC-TR-94-009
Complexity Issues in Discrete Hopfield Networks
Patrik Floreen and Pekka Orponen
NC-TR-94-010
Computational Complexity of Neural Networks:
a Survey
PEKKA ORPONEN
NC-TR-94-011
Valid Generalisation from Approximate Interpolation
Martin Anthony, Peter Bartlett, Yuval Ishai, John Shawe-Taylor
NC-TR-94-012
A Note on Testing the Resultant
T. Lickteig, K. Meer
NC-TR-94-013
Function Learning from Interpolation
Martin Anthony, Peter Bartlett
NC-TR-94-014
Learning Minor Closed Graph Classes with Membership
and Equivalence Queries
John Shawe-Taylor, Carlos Domingo, Hans Bodlaender, James Abello
NC-TR-94-015
Grammar Inference and the Minimum Description
Length Principle
Peter Grunwald
NC-TR-94-016
On-line learning with minimal degradation
in feedforward networks
V. Ruiz de Angulo and Carme Torras
NC-TR-94-017
Bounds for the Computational Power and Learning
Complexity of Analog Neural Nets
Wolfgang Maass
NC-TR-94-018
On the Complexity of Function Learning
Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
NC-TR-94-019
Neural Nets with Superlinear VC-Dimension
Wolfgang Maass
NC-TR-94-020
Efficient Agnostic PAC-Learning with Simple
Hypotheses
Wolfgang Maass
NC-TR-94-021
On the Computational Complexity of Networks
of Spiking Neurons
Wolfgang Maass
NC-TR-94-022
Sample Sizes for Sigmoidal Neural Networks
John Shawe-Taylor
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